基于小波变换动态时间规整的近红外光谱模型传递方法

为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方法将光谱进行小波变换预处理,然后利用动态时间规整算法(Dynamic time warping,DTW)找到近红外光谱波长点之间最优的对应关系并建立回归方程。使用近红外药品光谱数据集和汽油数据集建立传递模型,验证了基于小波变换动态时间规整模型传递方法的有效性。汽油光谱数据集C7、C8、C9和C10成分的预测标准偏差(SEP)分别为0.414 4、0.801 1、1.090 4和1.290 8;药品光谱数据集活性、硬度和重量的SEP分别为2.585 6、0.434 5和2.270 3,均小于传统方法。上述实验结果表明,所建立的模型传递方法能有效消除源机光谱和目标机光谱之间的差异,提高模型的稳定性和准确性,实现模型传递的效果。     

正交信号校正用于傅里叶变换红外光谱的模型传递

利用正交信号校正（OSC）实现了4组分气体混合物的PLS模型,在两台傅里叶变换红外光谱（FTIR）仪上的传递,并与直接标准化（DS）、分段直接标准化（PDS）、多元分散校正（MSC）和有限脉冲响应（FIR）的传递效果进行了比较,确立了以源机的校正模型直接对目标机数据进行预测的传递方式。经过OSC校正,预测均方根误差（RMSEP）为10^-3左右。OSC可以有效地减小测量仪器间的差异,同时使PLS模型的潜变量个数降为4,使模型简单化。与DS和PDS相比,OSC不需要同一样品在两台仪器上测量,在支集大小为3时,得到一致的预测准确度,表现出稳健性。MSC和FIR处理的RMSEP为10^-1左右,效果远差于OSC。     

基于一元线性回归的近红外光谱模型传递研究

为解决近红外光谱分析中的模型传递问题,本研究提出了一元线性回归直接标准化算法(Simple linear regression direct standardization,SLRDS)。为验证算法的有效性,采用玉米样品的近红外光谱集进行实验,并与传统的直接标准化算法(Direct standardization,DS)、分段直接标准化算法(Piecewise direct standardization,PDS)进行比较。实验结果表明,SLRDS算法不仅能够有效消除近红外光谱仪之间的差异,很好地实现玉米样品的PLS校正模型在3台仪器之间的共享,而且与DS和PDS算法相比,具有传递性能高、模型简单及所求参数少等优点。     

小波变换结合多维偏最小二乘方法用于近红外光谱定量分析

将小波变换和多维偏最小二乘法相结合用于近红外光谱定量校正模型的建立.首先将原始光谱进行小波变换分解,得到系列小波细节系数,通过选取一组受外界因素少、信息强的小波系数组成三维光谱阵,然后再采用多维偏最小二乘法建立校正模型.实验结果表明,该方法所建近红外校正模型的预测能力更强,并更具稳健性.     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R2cv)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

基于稳定一致波长筛选的无标样近红外光谱模型传递方法

测量环境及仪器间光谱信号的差异导致近红外光谱模型从主机传递到从机后,经常会产生过大误差。本研究提出了一种基于稳定一致波长筛选的无标样近红外模型传递方法(Screening stable and consistent wavelengths,SSCW),剔除主从仪器间差谱的标准偏差大于样品精密度测试光谱标准偏差的波长,以及精密度测试偏差过大的波长,筛选出仪器间光谱信号一致性好且稳定的波长建立近红外光谱定标模型。分别以玉米和黄芩样本集对本算法的有效性进行了检验。结果表明,SSCW模型传递后对从机样品的预测均方根残差RMSEP较全波长PLS模型直接传递结果小一个量级,大部分情况下优于分段直接校正算法(Piecewise direct standardization,PDS)的结果和文献报道的无标样模型传递结果。本方法具有传递性能好、模型参数少、稳健等优点,在不同仪器间可实现近红外光谱模型的无标样传递。     

基于正交信号校正的秸秆青贮饲料粗蛋白近红外分析模型传递方法

探讨了基于不同数据预处理方法的正交信号校正在秸杆饲料近红外光谱模型传递中的应用.以141个秸杆青贮饲料样品为研究对象,以其粗蛋白含量为目标参数,研究了基于无处理、局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正,在源仪器(SPECTRUM ONE NTS)和目标仪器1(ANTA-RIS)与目标仪器2(FOSS 6500)之间的模型传递效果.实验表明:对于两台傅里叶变换型近红外光谱仪,采用局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正均可成功实现模型传递,其中局部中心化和全局中心化法的作用效果基本一致,且优于Z-score标准化法.对于傅立叶变换和光栅型近红外光谱仪,全局中心化的作用效果明显优于其它3组处理效果,且只有全局中心化预处理的正交信号校正传递后的模型可用于实际预测.     

基于稳定一致波长优选的综纤维素近红外分析模型传递

以实现纸浆材综纤维素含量的近红外分析模型在3台不同型号光谱仪上共享为目标,提出SWCSS-UVE及SWCSS-CARS联用算法。即分别利用竞争性自适应重加权采样算法（CARS）和无信息变量剔除（UVE）算法,减少SWCSS方法中入选的无信息或信息少波长的不利影响,以提高模型转移精度,并与单独的SWCSS和分段直接标准化算法（PDS）以及斜率截距（S/B）算法校正后的传递结果进行比较。结果表明,通过SWCSS-UVE方法最终可从稳定一致光谱信号中进一步优选出91个波长建立模型,该模型能同时应用于2台从机所测量光谱的分析,预测标准偏差（RMSEP）分别从模型转移前的2.0114和9.4518下降到了1.5919与1.6818,优于SWCSS, SWCSS-CARS和PDS以及S/B算法的结果。这表明SWCSS-UVE算法可以有效剔除SWCSS方法中包含的无效波长,简化模型传递过程,提高模型传递效率和稳健性。     

小波变换用于近红外光谱数据压缩

近红外光潜数据量大,需要较大数据存储空间和较长的建模时间、本文以成品柴油性质分析为例．将小波变换用于近红外光谱数据压缩处理,详细考察了小波压缩参数,比较了压缩前后潜图差异以及性质分析偏差的变化。研究结果表明．采用Daubechies小波函数(N=2)为母函数．进行3次分解,直接采用其逼近系数(Ca3)作为谱图压缩数据,其重构光谱与原始光谱基本一致直接利用逼近系数进行性质分析,其分析精度与原始光谱数据基本相当,存储空间减少至原来的1／8,且能够明显缩短其建模时间和分析时间。     

复烤片烟常规化学成分的傅里叶变换近红外光谱法的模型转移

为实现复烤片烟常规化学成分的模型在不同品牌傅里叶变换近红外仪器上的使用与共享,以贵州产区复烤片烟样品为研究对象,利用Kennard-Stone算法选择标准样品,将偏移量校正(BC)、截距斜率校正(SBC)和光谱空间转换(SST)等3种模型转移算法应用于不同品牌傅里叶变换近红外仪器的模型转移,并对3种模型转移算法的转移结果进行分析。结果表明:将复烤片烟常规化学成分的主机模型直接应用于从机预测时,主机和从机的预测值之间存在显著性差异;采用BC、SBC和SST可以实现不同品牌傅里叶变换近红外仪器的模型转移,其中SST转移结果最优。     

对直接标准化算法的改进及其应用

由于各种仪器之间存在差异,主机上建立的定量模型用于从机会导致预测结果出现较大偏差。目前主要通过有标样方法和无标样方法来减小预测偏差。该文对现有标样方法中的直接标准化算法进行改进,在转移矩阵的建立过程中,对从仪器数据矩阵进行主成分分解,以预测均方差为判定标准,确定最终的转移矩阵。并以玉米和烟草数据为对象,测试了该法的有效性。玉米样品含有2种成分:水分和蛋白质;烟草样品含有4种成分:还原糖、总糖、总氮和总碱。结果表明,对于玉米样品中的2种成分,采用改进的方法可显著提高预测的准确度;对于烟草中的4种成分而言,采用改进的方法可获得稳健的预测结果。     

Calibration transfer of near‐IR partial least squares property models of fuels using virtual standards

Partial least squares (PLS) models of 10 important jet and diesel fuel properties were built using spectra from a master near‐IR dispersive instrument and then subsequently transferred to a secondary dispersive instrument via a novel calibration transfer method using virtual standards and a slope‐bias correction. Implementation of the transfer requires that only seven spectra of neat solvents be acquired on the master and secondary instruments. The spectra of the neat solvents are then used to digitally replicate spectra from the calibration set to generate virtual standards. Comparison of PLS predictions for the master and secondary instrument virtual standards provides a simple but effective slope‐bias correction for transfer. The transferred fuel properties include American Petroleum Institute gravity, % aromatics, cetane index, flashpoint, hydrogen content, % saturates, and distillation temperatures at 10%, 20%, 50%, and 90% volume recovered. Transfer error was lower than using either the pure solvents with a slope‐bias correction or than using a piecewise direct standardization calibration transfer using fuel spectra. Transfer error was higher than when using actual fuels to transfer the calibration. The use of virtual standards eliminates the need to maintain either complex fuel standards or the master instrument for future instrument calibration transfers. Copyright © 2011 John Wiley & Sons, Ltd.     

基于尺度不变特征变换筛选稳定特征波长的近红外光谱模型传递方法

该研究利用一维尺度不变特征变换(SIFT)算法寻找烟叶近红外光谱(Near infrared spectroscopy,NIRS)的稳定特征波长,根据样品精密度测试光谱筛选的波长计算重现率和重现度,采用L_9(3~3)正交表优化SIFT算法中的相关参数,使重现率和重现度尽可能高。基于优化的参数和主机上10个代表性样品的光谱,筛选出10个稳定特征波长集合,以这些波长集合并集的光谱响应为自变量,采用偏最小二乘(PLS)方法构建烟叶总植物碱NIRS模型(简称SIFT-PLS)。该模型直接传递到3台从机后,对3台从机样品总植物碱的平均相对预测误差(MRE)均满足小于6%的企业内控要求,而全光谱模型(WW-PLS)直接转移后仅1台从机的MRE满足要求,经分段直接校正(PDS)方法校正从机光谱后,WW-PLS模型也仅对1台从机的MRE小于6%。采用SIFT算法筛选稳定特征波长建立的NIRS模型可在3台从机直接共享,无需转移集,不需对从机光谱或光谱模型进行校正,实现了真正意义的无标样NIRS模型的直接转移。     

波长筛选结合直接校正法用于近红外光谱模型传递研究

该文提出了一种新的传递方法模型--波长筛选结合直接校正法(WSDS).首先利用样品的性质信息筛选出最有代表性的波长点信息,然后用直接校正法消除这些信息中包含的仪器间差异,以预测标准偏差(SEP)考察模型传递的效果.利用此算法对航空煤油的近红外光谱分析模型在不同仪器之间进行传递研究.经WSDS校正后,对航空煤油密度预测的...     

Near-infrared calibration transfer based on spectral regression

A calibration transfer method for near-infrared (NIR) spectra based on spectral regression is proposed. Spectral regression method can reveal low dimensional manifold structure in high dimensional spectroscopic data and is suitable to transfer the NIR spectra of different instruments. A comparative study of the proposed method and piecewise direct standardization (PDS) for standardization on two benchmark NIR data sets is presented. Experimental results show that spectral regression method outperforms PDS and is quite competitive with PDS with background correction. When the standardization subset has sufficient samples, spectral regression method exhibits excellent performance.     

偏最小二乘近红外光谱模型中潜变量个数对模型传递性能的影响

以玉米中水分、蛋白质、脂肪和淀粉4种主要成分含量以及烟叶总植物碱的偏最小二乘近红外光谱(PLS-NIRs)模型传递为例,考察了模型中潜变量个数(nLVs)对模型传递误差的影响。研究发现,根据累积贡献率大于99.9%确定的玉米、烟叶样品PLS-NIRs模型的nLVs分别为1和13,nLVs=1时建立的玉米模型对两台从机样品4个成分的预测值和主机预测值的重现性指标均满足国标要求;nLVs=13时建立的烟叶总植物碱模型经分段直接校正(PDS)后,可使4台从机样品的平均相对预测误差(MRE)小于6%。采用留一交叉验证或四折交叉验证确定的玉米、烟叶PLS-NIRs模型的nLVs分别为5～10,16与19,在这些nLVs下建立的玉米PLS-NIRs模型对从机样品的预测误差显著增大,超过许可的误差范围,且模型即使经PDS校正后,从机样品预测值与主机样品预测值的重现性指标大多不满足国标要求;nLVs13时所建烟叶总植物碱PLS-NIRs模型的转移误差随nLVs增大而增大,且PDS校正后不能保证模型对所有从机样品的MRE小于6%。根据累积贡献率大于99.9%或接近99.9%为准则选取nLVs,可有效避免过拟合,提高NIRs模型的传递性能。     

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

The application of mobile near-infrared (NIR) spectrometers in field measurements is growing. Calibration transfer techniques offer simple solutions for enabling models constructed on benchtop instruments for use on mobile spectrometers. Since different types of spectrometers with different components, scanning ranges and resolutions cause great differences in the spectral response, calibration transfer is difficult to apply. In this paper, we focus on calibration transfer among benchtop, portable and handheld spectrometers by a method of calibration transfer based on canonical correlation analysis (CTCCA). Its capability was illustrated by the example of a group of NIR spectra dataset for predicting reducing sugars, total sugar, and nicotine contents in tobacco leaves. The experimental results showed that the transferability of CTCCA was superior to other conventional calibration transfer methods, including piecewise direct standardization, spectral space transformation, calibration transfer based on independent component analysis, and calibration transfer based on the weight matrix. Moreover, the best transfer results were obtained in the three cases by canonical correlation analysis method executing transfer while the spectra were not interpolated, which shows that this approach has the advantage of easy implementation for calibration transfer. Therefore, CTCCA without interpolation calculation offers a new and simple solution for transferring the spectra acquired by mobile spectrometers to the optimized spectral models built on benchtop devices to improve the accuracy of the results. Additionally, the results show that the benchtop spectrometer is more suitable as the master instrument for calibration transfer with more accurate prediction than using a portable device as the master.     

Biofuels–Renewable Energy Sources: A Review

Biodiesel is biodegradable and nontoxic, and it significantly reduces toxic and other emissions when burned as a fuel. The advantages of biodiesel as diesel fuel are its portability, ready availability, renewability, higher combustion efficiency, non-toxicity, higher flash point, and lower sulfur and aromatic content, higher cetane number, and higher biodegradability. The major disadvantages of biodiesel are its higher viscosity, lower energy content, higher cloud point and pour point, higher nitrogen oxide (NO_x) emissions, lower engine speed and power, injector coking, engine compatibility, high price, and greater engine wear. The technical disadvantages of biodiesel/fossil diesel blends include problems with fuel freezing in cold weather, reduced energy density, and degradation of fuel under storage for prolonged periods. The sources of biodiesel are vegetable oils and fats. The direct use of vegetable oils and/or oil blends is generally considered to be unsatisfactory and impractical for both direct injection and indirect type diesel engines because of their high viscosities and low volatilities injector coking and trumpet formation on the injectors, higher level of carbon deposits, oil ring sticking, and thickening and gelling of the engine lubricant oil, acid composition. Biodiesel is obtained by transesterifying triglycerides with methanol. A popular variation of the batch transesterification process which needs high alcohol/acid ratio (several separation problems and high corrosivity and toxicity) is the use of continuous stirred tank reactors in series. This continuous process is heterogeneous and is based on reactive distillation. The key factor is the selection of the right and effective solid catalyst which leads to reduction of energy consumption and investments at all.